Image processing method and apparatus

ABSTRACT

In a method of and an apparatus for processing image data representing an image saliency data for the image is generated by determining a series of features of the image and the determined features are used to generate a probability measure for each point of the image representative of a location of a subject of the image. The saliency data is processed using respective ones of weighting functions of a plurality of spatial scales in order to determine the positions of regions of interest of the image at respective ones of the scales. Response data is generated for each scale representing the relative strength of response of the saliency data at the positions of the determined regions of interest to the function at that scale.

RELATED APPLICATIONS

The present application is based on, and claims priority from, GBApplication Number 0501890.8, filed Jan. 31, 2005, the disclosure ofwhich is hereby incorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The present invention relates to the field of image processing

BACKGROUND

It is known to be able to zoom in on a portion of an image capturedusing a digital camera or videocamera. For example, digital camerascommonly include a screen upon which an image can be previewed beforecapture, and viewed post-capture. The screen can be used to manuallyzoom into and view portions of a captured image at preset magnificationfactors for example.

Some devices provide a semi-automatic zoom function. Generally, suchsystems automatically zoom into a captured image by a predeterminedamount following image capture. A user may initiate such a function bypressing a button on the device following capture of an image, and amagnified portion of the captured image is then displayed. For example,a central portion of the image may be displayed at increasedmagnification since, in such systems, this is generally taken to be thearea in which salient material in the image is located.

Techniques exist for determining an area of potential saliency in animage, and these have been applied to systems in order to effectautomatic cropping of the image. For example, U.S. Pat. No. 6,654,507describes a method for cropping a portion of an image by identifying apotential main subject of the image. A belief map is generatedcomprising image features, each feature having a measure assignedthereto representative of the probability that it is the main subject ofthe image. The image is cropped to include the feature with the highestprobability.

Further techniques exist for providing automatic cropping of an image inresponse to a determination of salient portions in the image. Forexample, European Patent Application No. 02251070.5.

In, Yu-Fei Ma, Hong-Jiang Zhang, “Contrast-based image attentionanalysis by using fuzzy growing,” Proceedings of the eleventh ACMinternational conference on Multimedia, November 2003, a saliency map iscreated using normalized local contrast measures. Salient regions areidentified using a region growing method.

In Xian-Sheng Hua, Lie Lu, Hong-Jiang Zhang, “Automatically convertingphotographic series into video,” Proceedings of the 12th annual ACMinternational conference on Multimedia, October 2004, saliency locationsareas found using Yu-Fei Ma, Hong-Jiang Zhang, “Contrast-based imageattention analysis by using fuzzy growing,” Proceedings of the eleventhACM international conference on Multimedia, November 2003, are used togenerate a dynamic viewing path. The method designates saliencylocations as keyframes and then uses explicit film making rules to placethem in a preferred order.

Also in this connection, WO GB01/05683 provides a system for determiningsalient portions in an image and generating an automatic rostrum pathfor the image in order that salient portions can be displayed along thepath.

SUMMARY OF THE INVENTION

According to a first aspect of the present invention there is provided amethod of processing image data representing an image, the methodcomprising generating saliency data for the image by determining aseries of features of the image and using the determined features togenerate a probability measure for each point of the imagerepresentative of a location of a subject of the image, processing thesaliency data using respective ones of weighting functions of aplurality of spatial scales in order to determine the positions ofregions of interest of the image at respective ones of the scales, andgenerating response data for each scale representing the relativestrength of response of the saliency data at the positions of thedetermined regions of interest to the function at that scale.

According to a second aspect of the present invention there is provideda method of processing image data representing an image, the methodcomprising generating saliency data by determining a series of featuresof the image and using the determined features to generate a probabilitymeasure for each point of the image representative of a location of asubject of the image, convolving the saliency data with a set offunctions of respectively different spatial scales in order to generateresponse data representing a saliency density for the image atrespective ones of the spatial scales, determining from the responsedata spatial maxima in the saliency density across the respective scalesfor respective ones of the determined features, and selecting, using thespatial and scale location of a determined maxima, image datarepresenting a salient image portion at that scale.

According to a third aspect of the present invention there is providedapparatus comprising a processor operable to process image datarepresenting an image, the processor operable to generate saliency datafor the image by determining a series of features of the image and usingthe determined features to generate a probability measure for each pointof the image representative of a location of a subject of the image,process the saliency data using respective ones of weighting functionsof a plurality of spatial scales in order to determine the positions ofregions of interest of the image at respective ones of the scales, andgenerate response data for each scale representing the relative strengthof response of the saliency data at the positions of the determinedregions of interest to the function at that scale.

According to a fourth aspect of the present invention there is provideda computer program product for use with a computer, said computerprogram product comprising a computer useable medium having computerexecutable program code embodied thereon, wherein said product isoperable, in association with said computer, to generate saliency datausing image data representing a captured image by determining a seriesof features of the image and use the determined features to generate aprobability measure for each point of the image representative of alocation of a subject of the image, process the saliency data usingrespective ones of weighting functions of a plurality of spatial scalesin order to determine the positions of regions of interest of the imageat respective ones of the scales, and generate response data for eachscale representing the relative strength of response of the saliencydata at the positions of the determined regions of interest to thefunction at that scale.

According to a fifth aspect of the present invention there is provided acomputer program, comprising machine readable instructions, wherein saidprogram is arranged, in association with said machine, to generatesaliency data using image data representing a captured image bydetermining a series of features of the image and using the determinedfeatures to generate a probability measure for each point of the imagerepresentative of a location of a subject of the image, process thesaliency data using respective ones of weighting functions of aplurality of spatial scales in order to determine the positions ofregions of interest of the image at respective ones of the scales, andgenerate response data for each scale representing the relative strengthof response of the saliency data at the positions of the determinedregions of interest to the function at that scale.

According to a sixth aspect of the present invention there is provided amethod of processing image data representing an image captured using animage capture device, the method comprising processing the image data inorder to generate saliency data representing salient portions of theimage, processing the saliency data at a plurality of spatial scalesusing respective ones of a plurality of weighting functions, theprocessing operable to generate response data representative of ameasure of the relative saliency of portions of the image at respectiveones of the spatial scales, using the response data in order todetermine a preferred scale for the salient portions of the image bydetermining maxima in the response data for respective ones of thesalient portions, and generating region data representing respectiveones of the salient portions at their preferred scales, using the regiondata to generate a visualisation path which substantially traversesrespective ones of the salient portions, wherein the path is generatedon the basis of the scale of the salient portions, and the relativedistance between salient portions.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the invention, and to further highlightthe ways in which it may be brought into effect, reference is made, byway of example only, to the following figures in which:

FIG. 1 is a schematic representation of an image capture device operablein accordance with an embodiment;

FIG. 2 a is an exemplary image captured using the device of FIG. 1 andcomprising a foreground with an image subject, and a background;

FIG. 2 b is a schematic representation of the relative saliency ofrespective points of the image in FIG. 2 a;

FIG. 3 is a flow chart of the process according to a preferredembodiment using data obtained from the saliency map of FIG. 2 b;

FIG. 4 is a schematic representation of an exemplary kernel;

FIG. 5 is a schematic representation of a set of centre-surroundresponse maps corresponding to respective kernels applied to thesaliency map of FIG. 2 b;

FIGS. 6 a to 6 c are schematic representations depicting a process togenerate a set of centre-surround response maps from the saliency map ofFIG. 2 b using suitable kernels;

FIG. 7 is a schematic representation of an image including superimposedregions of interest;

FIG. 8 is a flow diagram representing an exemplary procedure forgenerating a rostrum path;

FIG. 9 is a schematic representation of an image including superimposedregions of interest, and the rostrum path; and

FIG. 10 is a flow diagram representing an exemplary procedure fordetermining salient portions of an image and generating a rostrum pathto traverse such portions.

The terms “comprises” or “comprising when used in this specificationspecify the presence of stated features, integers, steps or componentsbut do not preclude the presence or addition of one or more otherfeatures, integers, steps, components or groups thereof.

DETAILED DESCRIPTION

FIG. 1 is a schematic representation of an image capture device 100operable in accordance with an embodiment. The device 100 can be adigital or analogue still or video camera or combination thereof.Alternatively, the device 100 may form part of a mobile station, such asa mobile telephone for example, or a PDA. Other suitable alternativesare possible.

The device 100 comprises an image capture element 101 such as a CCD,CMOS or other suitable device operable to generate image datarepresentative of an image captured (or capturable) by the device 100.The captured image (or a preview of an image) can be presented to a useron a display of device 100 (not shown). Device 100 further comprises amemory 102 which can be a dynamic random-access memory (DRAM) and cancomprise non-volatile memory (e.g. flash, ROM, PROM, etc.) and/orremovable memory (e.g. memory cards, disks, etc.) for example. Memory102 can store raw image digital data as well as processed image digitaldata processed by a suitable processor 103 of device 100. Device 100 cancomprise a digital signal processor (not shown) in addition to processor103. Alternatively, processor 103 can be adapted to perform anynecessary digital signal processing operations. Device 100 comprises abus, or similar, 104 to connect the various device components as iscustomary.

Device 100 is operable to capture an image of a scene or object asdesired. An exemplary captured image is depicted in FIG. 2 a. The image200 of FIG. 2 a comprises a background area, generally denoted by 201,and a subject of the image generally denoted by 202—the two childrenplaying. According to a preferred embodiment, salient areas of the image200 are determined using known methods. For example, the method of Itti,Koch and Niebur (“A Model of Saliency-based Visual Attention for RapidScene Analysis”, IEEE Transaction on Pattern Analysis and MachineIntelligence, 20(11), 1254-1259, 1998, which is incorporated herein byreference in its entirety) can be used in order to derive a ‘saliencymap’ for the image 200. Such a saliency map is depicted in FIG. 2 b. Thesaliency map 203 provides a measure representative of the relativesaliency—or potential ‘interesting-ness’—of respective points of image200. The saliency map 203 is a schematic representation of an abstractconcept generally embodied by data which represents the relativesaliency of respective points of the image 200, and is not intended tobe limiting in this respect. The saliency map (data) may be generated bydevice 100 using processor 103 using the above method, and stored inmemory 102 as appropriate. It will be appreciated by those skilled inthe art that other methods for determining, and displaying if necessary,a suitable saliency map or data, or other similar measure, exist, andcan be used instead of the above.

FIG. 3 of the accompanying drawings is a flow chart representing aprocedure to generate data according to a preferred embodiment. Data 301representing the relative saliency of respective points of image 200 asembodied by the saliency map in FIG. 2 b is input to processor 103 atstep 300. The data 301 is processed in order to generate data 303. In apreferred embodiment, data 303 is generated using a set of kernels(functions) which are convolved with the data 301 in a conventionalknown way. The functions represent kernels which have substantiallypositive coefficients in an inner spatial region thereof, andsubstantially negative coefficients in an outer spatial region thereof.In a preferred embodiment, all kernel coefficients are constant. Aschematic representation of an exemplary kernel is shown in FIG. 4. Thekernel is in the form of a ‘mexican hat’. In practice, the convolutionof the function with saliency data can proceed by ensuring that at eachpoint of a saliency map with which convolution is to occur, the mexicanhat has portions above and below (in a spatial sense) the image plane.This then allows an approximation of the derivative of the saliency mapat each point convolution occurs. The function can have a form:${\sim\left( {2 - \frac{r^{2}}{a^{2}}} \right)}{\mathbb{e}}^{- \frac{r^{2}}{2a^{2}}}$

where r²=x²+y² and ‘a’ is a constant. Other alternatives are possible,and the above is not intended to be limiting.

Data 303 represents the response of data 301 to respective ones of thekernels, and more specifically the response of data falling within thesupport of a kernel centred at respective pixel locations of image 200.According to an embodiment, data 303 can be termed response data, orcentre-surround response data, referring to the fact that the responseat a point, or pixel location, of image 200 is determined based on theconvolution of respective ones of kernels centred at that location withdata falling within the support of the kernel at that point. Thecentre-surround response data can be used to form a set ofcentre-surround maps such as those illustrated in FIG. 5 at 500. Each ofthe maps 500 of FIG. 5 represents the response at respective locationsof image 200 at the scale of the kernel used to generate the data forthe relevant map—arrow 501 shows the direction of increasing scale ofthe maps 500 of FIG. 5. The peaks of a map of FIG. 5 represent theregions of greatest response of the image to convolution with a kernelat the relevant scale. The location of salient structures (regions) inan image is therefore indicated by maxima in the spatial derivative of aresponse map, whereas a natural size (scale) of the structurecorresponds to maxima of the scale-space derivative of the response atthat spatial location (see for example, T. Lindenberg, Scale-SpaceTheory in Computer Vision, Kluwer Academic Publishers, 1994, which isincorporated herein by reference in its entirety). The saliency map alsoindicates potential portions of salient material in the image.

FIGS. 6 a to 6 c are schematic representations depicting an exemplaryprocess used to generate the set of centre-surround response maps fromthe saliency map of FIG. 2 b using suitable kernels. Each kernel600,601,602 comprises an inner and outer region comprising coefficientswith substantially positive and negative coefficients respectively, asexplained above. The inner regions for kernels 600,601,602 are depictedat 603,604,605 respectively, and the outer regions are depicted at606,607,608 respectively. It will be appreciated by those skilled in theart that other kernels may be used, and the above is not intended to belimiting. Each kernel 600,601,602 is applied at (convolved with) everylocation (pixel) of the saliency map of FIG. 2 b, and a scale space ofthe responses is created by using kernels of different spatial sizes. Inthe case of FIG. 5 three such kernels have been used. Additional orfewer can be used.

With reference to FIGS. 6 a to 6 c, and the corresponding image in FIG.2 a, it can be seen that spatial maxima of the centre-surround maps 600correspond to the location of salient clusters at that particular scale,and the maxima of a response map can therefore be characterised asrepresenting salient clusters at the natural scale at which thoseclusters occur in the image.

Hence, salient portions of a captured image are determined at aplurality of spatial scales. The salient portions, or regions ofinterest, can then be used as candidates for a rostrum path generation.Determining regions of interest at a plurality of scales enables adetermination to be made about how much to zoom into an image withoutneglecting large scale structures of the image. Hence the location ofstructures in an image at a particular scale is indicated by a maximumin the spatial derivative of the response map of the scale in question.The natural size (scale) of a structure corresponds to maxima of thescale-space derivative of the response at that spatial location.

Once salient portions of an image have been determined as explainedabove, the results can be used to enable a user of an image capturedevice to sample the salient portions. As explained, the provision ofdetermining salient portions of an image at various spatial scales helpsto ensure that substantially all relevant parts of an image are sampledin a rostrum display, and in a preferred order so that relevant materialin an image is not ‘overlooked’ during the visual tour of the image. Anarea around a determined salient portion can be used in addition to theportion itself in order to provide context for the sampled portion.Preferably a bounding box of dimensions at least large enough toencompass the salient portion can be used in this respect. The aspectratio of the box can be determined with consideration of the device uponwhich the portions are to be displayed, for example.

According to a preferred embodiment, the provision of sampling salientportions of a captured image proceeds in the form of a rostrum, in whichareas of an image are viewed at an increased magnification factor withrespect to the originally captured image by a notional camera whichmoves across the image—the view ‘seen’ by this notional camera isdisplayed to a user as the rostrum view of the image. The transitionbetween displayed areas can proceed in a number of ways. For example, avelocity of the notional camera can take a quadratic form betweensalient portions such that a salient portion is dwelled upon for apredetermined amount of time followed by increasing in velocity to thenext salient portion to be displayed and a corresponding decrease invelocity as this portion is approached and so on. Other alternatives arepossible such as a linear velocity between salient areas for example.

Preferably, areas of an image which have been determined as containingpotential salient material at a larger spatial scale are displayedfirst, with respective areas determined as containing potential salientmaterial at progressively smaller spatial scales being displayedthereafter. The distance between respective salient areas can also betaken into account when determining a suitable path. For example, if asmaller region is closer to the region under display than a largerregion, the smaller region can be displayed in preference to the largerregion. This helps to ensure that a generated path produces a pleasingeffect, and does not simply move back and forth between regions basedsolely on their size—distance between regions is also a factor.

This helps to ensure that no relevant detail in the image is missedduring the sampling process, and that a pleasing result is achieved.

FIG. 7 is a schematic representation of a captured image 700.Superimposed onto the image are areas 701, 702, 703 and 704 whichrepresent bounding boxes surrounding salient portions of the image 700elucidated at respectively decreasing spatial scales. Preferably, thebounding boxes have substantially the same aspect ratio as the displayupon which the salient portions of an image are to be sampled, howeverthis need not be the case, and the bounding areas can be of any shape orconfiguration. Areas 701, 702, 703 and 704 are displayed using a displayof device 100 such that the area being displayed fills the display,thereby providing for magnification of the area. During transitions fromarea to area, displayed image portions may be displayed at progressivelyincreasing magnification. For example, the magnification factor betweenrelevant areas may be interpolated based on the factor of the area underdisplay, and the necessary factor of the area to be displayed such thata linear increase in magnification is provided during the transition.Other effects are possible.

According to a preferred embodiment, a rostrum path is generated acrossthe image such that areas 701, 702, 703 and 704 are displayed in orderof decreasing size (and hence increasing magnification factor). Area 701is the largest, with the size of the areas decreasing to 704 which isthe smallest of those depicted. As explained, it is desirable to displaythe largest area first, and gradually increase the magnification of theimage in order to display the rest of the areas in order of decreasingsize (and hence increasing magnification), whilst also taking intoaccount the distance between respective portions in order to avoidgenerating a path which moves back and forth between regions, or whichis longer than necessary. A guiding principle of least commitment isused—it is safer to show larger regions of interest first and thenprogressively move to smaller and smaller regions. For example, largerregions are very likely to contain the subject (or subjects) of animage, whereas smaller regions presented in the rostrum are more likelyto be incorrect in that they do not contain a main subject of an image.Therefore potentially irrelevant image details are not presented to auser first, but rather progressively more ‘risks’ are taken as therostrum path advances.

It is desirable to minimise the distance travelled by the notionalcamera between regions of interest. In this connection, areas to bedisplayed can be ranked according to their size and the relative(Euclidean) distance between them. Preferably, the distance betweendetermined salient areas in a generated rostrum path is minimised inorder to avoid the notional camera ‘dodging’ about the image.Advantageously, the rostrum path is a geodesic through the determinedregions of interest with larger areas at the beginning of the pathfollowed by progressively smaller areas along its length. Otheralternatives are possible.

FIG. 8 is a flow diagram representing an exemplary procedure forgenerating a suitable rostrum path for an image which has been capturedby a device such as that described above.

Starting from a default region of interest 800, which can be the fullcaptured image for example, all regions of interest (as determined usingthe method explained above) are ranked (801) based on the minimumtravelled Euclidean distance in the three-dimensional position/sizespace. Then, a four-dimensional space-time path is generated (rostrumpath, 802) which traverses all the determined regions of intereststopping at each of them for a preset time for example. Alternatively,the time spent at each region of interest can be dependent on a measureof the relative saliency of the region. The path between regions ofinterest is interpolated (803), and can be linear in position/size andparabolic in time as described above so that after a preset time (ifappropriate) it accelerates away from the current region of interest anddecelerates when approaching the next region of interest for example.Other alternatives are possible.

FIG. 9 is a schematic representation of an image 900 onto whichpotential regions of interest (salient areas) have been determined usingthe method as explained above, and which have been indicated bysuperimposing areas 901, 902, 903, 904 onto the image. The line 905represents a generated rostrum path according to a preferred embodiment.

The path 905 begins at 901, the largest of the regions of interest. From901, it can be seen that regions 902 and 903 are substantially the samedistance from 901. However, region 902 is larger in area than 903, andtherefore 902 is the next area to be displayed using the path 905. Fromregion 902, region 904 is nearest. However, it can be seen that region904 is smaller in size than region 903. Region 903 can be displayed nexton the path 905, with 904 being displayed last. Other alternatives arepossible. In particular, 904 can be displayed following region 902 with903 after that due to the distance between the portions. The transitionin between areas can proceed as explained above.

Hence, spatial clustering of a saliency data generated from image datarepresenting a captured image is used to determine salient regions ofthe image that can be used as candidates for a rostrum path generation.A number of ways can be used to perform clustering providing that theanalysis is performed at different scales.

To make a rostrum path more compelling some balanced risks can be takenon how much to zoom into details without neglecting large scalestructures in an image. Analysing saliency at multiple scales andchoosing which one best represents the data is therefore important.Accordingly, a weighting kernel of a given size (i.e. scale orbandwidth) is passed over an image (i.e. convolved with image data) andthe response represents a map indicating ‘how much’ saliency (thedensity) there was under the kernel. The kernel can be one if the knownsmoothing kernels such as a Gaussian function for example. Kernels ofvarious sizes are passed over an image in order to calculate thesaliency density at different scales.

Using scale space theory principles, the location of structures in animage is indicated by maxima in the spatial derivative of a responsemap, whereas natural size (scale) corresponds to maxima of thescale-space derivative of the response at that spatial location.

The derivative for a particular scale can be approximated using a‘centre-surround’ operator where the response of the saliency data to asmaller (inner) kernel is subtracted from the response to a larger(outer) kernel. This is advantageous where memory and processingconsiderations must be taken into account. When generating a rostrumpath, larger areas are very likely to contain the subject (or subjects)whereas smaller regions are more likely not to. Hence, it is appropriateto make sure that potentially irrelevant details are not presented to auser at the beginning of a generated path, but that progressively more‘risks’ are taken as the path is traversed such that smaller regions areshown towards the end of the path.

The above described can be used in connection with image capture devicesas explained or with interactive viewing systems such as televisions,PDAs, mobile telephones etc. In each case, it is desirable, that shouldthe system in question begin to display irrelevant or undesirablecontent that a user can press a button (or similar, including voicecommands etc) and exit the automatic viewing. Such an auto-rostrumsystem can be employed in systems where there is a limited userinterface and/or a requirement for user passivity (e.g. televisionviewing, digital photo frames etc) together with a low resolutiondisplay or excessive viewing distance for example.

In this connection, FIG. 10 is a flow diagram representing an exemplaryprocedure for determining salient portions of an image and generating arostrum path to traverse such portions. At 1001 saliency data is inputto a processor, such as a processor of the image capture device asdescribed above. At 1002 the saliency data is used to generatecentre-surround data corresponding to the response of the saliency datato convolution (or similar) with a plurality of centre-surround kernels(functions) at a plurality of different spatial scales. At 1003,candidate salient locations in the image are determined by determiningmaxima in the centre-surround data. The best candidate regions areselected 1004 and then ranked 1005 according to some criterion such asspatial size (area for example) of the regions, and a visualisation pathis generated 1006 to substantially traverse the regions. At 1007 thepath is used in order to display regions of the image.

It will be appreciated by those skilled in the art that additionalfunctionality can be added to the above system. For example, a facedetection system can be added. Detected faces can be used in thegeneration of a visualisation path across the image in addition to themethod as described above. Detected faces could, for example, cause adeviation in a generated path which would otherwise not have been therein order that a face is presented for viewing early on in the path.Other alternatives are possible.

1. A method of processing image data representing an image, the methodcomprising: generating saliency data for the image by determining aseries of features of the image and using the determined features togenerate a probability measure for each point of the imagerepresentative of a location of a subject of the image; processing thesaliency data using respective ones of weighting functions of aplurality of spatial scales in order to determine the positions ofregions of interest of the image at respective ones of the scales; andgenerating response data for each scale representing the relativestrength of response of the saliency data at the positions of thedetermined regions of interest to the function at that scale.
 2. Amethod as claimed in claim 1, further comprising: for each determinedregion of interest, using the response data to determine a maximumresponse over all scales for that region; and using the position in theimage corresponding to the determined maximum and the scale at which themaximum occurs to generate output image data representing a salientportion of the image at that scale and which corresponds to the regionof interest in question.
 3. A method as claimed in claim 2, wherein theoutput image data is displayed such that output image data representinga salient portion corresponding to a larger scale is displayed inpreference to image data representing a salient portion corresponding toa smaller scale.
 4. A method as claimed in claim 2, further comprising:generating path data representing a path which substantially traversesrespective ones of the salient portions at different scales; anddisplaying image data substantially on the path in an order such that asalient portion corresponding to a larger scale is displayed inpreference to a salient portion corresponding to a smaller scale.
 5. Amethod as claimed in claim 4, wherein the image data includes thedetermined salient portions.
 6. A method as claimed in claim 3, whereinsalient portions are displayed in an order dependent on theirdisposition within the image.
 7. A method as claimed in claim 6, whereindisplaying salient portions further comprises: for a salient portionunder display, determining the distance to other salient portions; andon the basis of the determination of distance, selecting a salientportion for subsequent display, wherein a portion which is closer to theportion under display is selected in preference to other portions.
 8. Amethod as claimed in claim 1, wherein the weighting functions are a setof centre-surround kernels of respectively different spatial scales. 9.A method as claimed in claim 8, wherein the kernels have substantiallypositive coefficients in an inner spatial region thereof, andsubstantially negative coefficients in an outer spatial region thereof.10. A method as claimed in claim 2, wherein the output data furthercomprises image data surrounding the determined regions of interest suchthat the output image data further provides a bounding box aroundrespective regions of interest.
 11. A method of processing image datarepresenting an image, the method comprising: generating saliency databy determining a series of features of the image and using thedetermined features to generate a probability measure for each point ofthe image representative of a location of a subject of the image;convolving the saliency data with a set of functions of respectivelydifferent spatial scales in order to generate response data representinga saliency density for the image at respective ones of the spatialscales; determining from the response data spatial maxima in thesaliency density across the respective scales for respective ones of thedetermined features; and selecting, using the spatial and scale locationof a determined maxima, image data representing a salient image portionat that scale.
 12. A method as claimed in claim 11, wherein the set offunctions are a set of centre-surround kernels adapted to comprisesubstantially positive coefficients in an inner spatial region thereof,and substantially negative coefficients in an outer spatial regionthereof.
 13. A method as claimed in claim 11, wherein the saliencydensity comprises a measure of the relative number of salient portionsfalling under the support of the function at the image area in question.14. A method of processing image data representing an image capturedusing an image capture device, the method comprising: processing theimage data in order to generate saliency data representing salientportions of the image; processing the saliency data at a plurality ofspatial scales using respective ones of a plurality of weightingfunctions, the processing operable to generate response datarepresentative of a measure of the relative saliency of portions of theimage at respective ones of the spatial scales; using the response datain order to determine a preferred scale for the salient portions of theimage by determining maxima in the response data for respective ones ofthe salient portions, and generating region data representing respectiveones of the salient portions at their preferred scales; using the regiondata to generate a visualisation path which substantially traversesrespective ones of the salient portions, wherein the path is generatedon the basis of the scale of the salient portions, and the relativedistance between salient portions.
 15. Image capture apparatuscomprising a processor, the processor operable to process image data inaccordance with the method as claimed in claim
 1. 16. Apparatuscomprising a processor operable to process image data representing animage, the processor operable to: generate saliency data for the imageby determining a series of features of the image and using thedetermined features to generate a probability measure for each point ofthe image representative of a location of a subject of the image;process the saliency data using respective ones of weighting functionsof a plurality of spatial scales in order to determine the positions ofregions of interest of the image at respective ones of the scales; andgenerate response data for each scale representing the relative strengthof response of the saliency data at the positions of the determinedregions of interest to the function at that scale.
 17. Apparatus asclaimed in claim 16, wherein the processor is further operable to: foreach determined region of interest, process the response data todetermine a maximum response over all scales for that region; and usethe position in the image corresponding to the determined maximum andthe scale at which the maximum occurs to generate output datarepresenting a salient portion of the image at that scale whichcorresponds to the region of interest in question.
 18. Apparatus asclaimed in claim 16, wherein the output image data is presented using adisplay of the apparatus such that output image data representing asalient portion corresponding to a larger scale is displayed inpreference to image data representing a salient portion corresponding toa smaller scale.
 19. Apparatus as claimed in claim 16, wherein theprocessor is further operable to: generate path data representing a pathwhich substantially traverses respective ones of the salient portions atdifferent scales; and render suitable for display image datasubstantially on the path in an order such that a salient portioncorresponding to a larger scale is displayed in preference to a salientportion corresponding to a smaller scale.
 20. Apparatus as claimed inclaim 18, wherein salient portions are displayed in an order dependenton their disposition within the image.
 21. Apparatus as claimed in claim20, wherein displaying salient portions further comprises: for a salientportion under display, determining the distance to other salientportions; and on the basis of the determination of distance, selecting asalient portion for subsequent display, wherein a portion which iscloser to the portion under display is selected in preference to otherportions.
 22. A computer program product for use with a computer, saidcomputer program product comprising: a computer useable medium havingcomputer executable program code embodied thereon, wherein said productis operable, in association with said computer, to generate saliencydata using image data representing a captured image by determining aseries of features of the image and use the determined features togenerate a probability measure for each point of the imagerepresentative of a location of a subject of the image; process thesaliency data using respective ones of weighting functions of aplurality of spatial scales in order to determine the positions ofregions of interest of the image at respective ones of the scales; andgenerate response data for each scale representing the relative strengthof response of the saliency data at the positions of the determinedregions of interest to the function at that scale.
 23. A logic circuitconfigured to operate in accordance with the method claimed in claim 1.